1. Field of the Invention
The present invention relates to intelligent systems, and in particular, to intelligent controllers using neural network based fuzzy logic.
2. Description of the Related Art
Uses of intelligent controllers have become more numerous and varied in keeping pace with the numerous and varied control requirements of complex modern electronics systems. For example, intelligent controllers are being called upon more frequently for use in assisting or use as servomechanism controllers, as discussed in commonly assigned U.S. patent applications Ser. No. 07/967,992 entitled "Intelligent Servomechanism Controller Using a Neural Network", and Ser. No. 07/859,328, entitled "Intelligent Controller With Neural Network and Reinforcement Learning" (the disclosures of which are each incorporated herein by reference). Further applications include control systems for robotic mechanisms.
One type of intelligent controller seeing increased use and wider application uses "approximate reasoning", and in particular, fuzzy logic. Fuzzy logic, initially developed in the 1960s (see L. A. Zadeh et al., "Fuzzy Sets and Applications", Selected Papers of L. A. Zadeh, by R. R. Yager, S. Ouchinnikov et al. (Eds.), John Wiley & Sons, 1987), has proven to be very successful in solving problems in many control applications where conventional model-based (mathematical modeling of the system to be controlled) approaches are very difficult, inefficient or costly to implement.
An intelligent controller based upon fuzzy logic design has several advantages including simplicity and ease of design. However, fuzzy logic design does have a number of disadvantages as well. As the control system complexity increases, it quickly becomes more difficult to determine the right set of rules and membership functions to accurately describe system behavior. Further, a significant amount of time is needed to properly "tune" the membership functions and adjust the rules before an accurate solution can be obtained. Indeed, for many complex systems, it may even be impossible to arrive at a satisfactory set of rules and membership functions. Moreover, once the rules have been determined, they remain fixed within the fuzzy logic controller, i.e. the controller cannot be modified, e.g. "learn", based upon its experience (except adaptive fuzzy logic systems which do allow some limited adaptation capabilities).
Furthermore, even once a good set of rules and membership functions has been established and used, the resulting solutions, i.e. control signal sets, must be evaluated in accordance with those rules and then "defuzzified". As is known, rule evaluation and defuzzification are two important steps in fuzzy logic design. Rule evaluation, or fuzzy inferencing as it may be called, combines the output from all of the rules which have "fired". However, the output of the fuzzy inferencing still remains as a fuzzy output. Defuzzification then converts this fuzzy output into numerical, i.e. nonfuzzy, outputs.
Several schemes have been proposed for fuzzy inferencing and defuzzification; however, all are based on some form of heuristics. For example, the most popular conventional fuzzy inferencing method uses the maximum of the outputs from all rules for each universe of discourse. The most popular and effective conventional defuzzification uses the center-of-gravity ("COG") method. For simple systems, these methods generally yield good solutions. However, for more complex systems, the heuristic based algorithms may not yield satisfactory results over a wide range of system control inputs. Indeed, conventional antecedent processing according to a "minimum" operation is often unsatisfactory for not providing consistently accurate outputs over a wide range of inputs and/or applications. A further demanding task is that of determining a good set of rules that will work well with those inferencing and defuzzification techniques.
The application of neural networks to learn system behavior has been suggested to overcome some of the problems associated with fuzzy logic based designs. Using a system's input and .output data, a neural network can learn the system behavior and, accordingly, generate fuzzy logic rules. See: B. Kosko, "Neural Nets and Fuzzy Systems", Prentice Hall 1992; J. Nie et al., "Fuzzy Reasoning Implemented By Neural Networks", Proceedings of IJCNN92 (International Joint Conference on Neural Networks, June 1992), pp. II702-07; and J. Buckley et al., "On the Equivalent of Neural Networks and Fuzzy Logic", Proceedings of IJCNN92, pp. II691-95.
As is known, a neural network mimics human learning instead of using fixed, preprogrammed approximate reasoning or rules. Also, like fuzzy logic, it uses numerical techniques. In a neural network, many simple processing elements are interconnected by variable connection strengths and the network learns by appropriately varying those connection strengths. It is primarily a data driven system and does not rely heavily upon programming. By proper learning, the neural network can develop good generalization capabilities, and therefore, solve many control problems that would otherwise go unsolved or be inefficiently solved by existing techniques.
However, a neural network may not always be the most effective way to implement an intelligent controller, since implementation of a neural network is more costly compared to fuzzy logic implementations. For example, fuzzy logic may be more effective for a particular application and, by proper programming, a conventional embedded controller can be used to implement the fuzzy logic. A neural network implementation by programming of the conventional embedded controller is also possible, but it will typically be significantly slower. Furthermore, a dedicated hardware implementation, generally more desirable, is more common for fuzzy logic than for a neural network, particularly when considering the relative costs of each.
Another problem with a neural network based solution is its "black box" nature, namely the relationships of the changes in its interlayer weights with the input/output behavior of the system being controlled. Compared to a fuzzy rule based description of the system, a good understanding of the "black box" nature of a neural network is difficult to realize.
Accordingly, it would be desirable to have an improved technique for applying neural network design to the design and implementation of fuzzy logic. Further, it would be desirable to have an improved fuzzy logic design in which antecedent processing, rule evaluation (fuzzy inferencing) and defuzzification can be performed upon control signals generated in accordance with such neural network based fuzzy logic design.